NeuroMold is an intelligent, neuromorphic edge computing framework designed to optimize real-time control in plastic injection molding systems using Spiking Neural Networks (SNNs). This project leverages bio-inspired computation to achieve ultra-low power, adaptive control on edge devices — mimicking the brain’s ability to respond dynamically to complex stimuli.
Simulation live at: https://superlative-seahorse-ea5599.netlify.app/dashboard
Plastic injection molding industries face major bottlenecks such as:
- ❌ High defect rates (30%+ rework due to undetected flaws)
- ⚡ Inefficient energy usage and material waste
- 🧱 Rigid legacy systems (SCADA, PLC) with no predictive capacity
- 🧠 Traditional ML models (ANNs) too heavy for real-time, on-device inference
NeuroMold proposes a next-gen solution: biologically inspired SNNs embedded in edge devices for real-time anomaly detection, defect prediction, and closed-loop adaptive control.
NeuroMold’s technical pipeline is modular and built for simulation and potential deployment:
Generates realistic time-series sensor data (pressure, temperature, etc.) to simulate factory conditions.
Custom encoder transforms continuous data into spike trains using temporal Poisson encoding, designed to work with SNNs.
A Norse-based LIF (Leaky Integrate-and-Fire) SNN processes spike inputs and generates optimal control signals in an energy-efficient, event-driven fashion.
A physics-informed model simulates actuator feedback loops, plastic flow, and thermal states to evaluate controller performance.
Quantitatively measures:
- Response latency
- Energy use (via spike counts)
- System stability & accuracy
Simulations/
├── data/
│ └── synthetic_injection_molding_dataset.csv
├── encoding/
│ └── spike_encoder.py
├── model/
│ └── snn_norse_controller.py
├── simulator/
│ └── injection_molding_simulator.py
├── results/
│ └── *.npy # Includes outputs from ANN, PID, SNN
├── visualizations/
│ └── output_charts/ # PNG graphs
├── run_pipeline.py
├── requirements.txt
└── NeuroMold_Report.md
Dashboard/
├── src/
│ ├── components/
│ ├── charts/
│ ├── context/
│ └── utils/
├── public/
├── index.html
├── tailwind.config.js
└── vite.config.ts
NeuroMold leverages Spiking Neural Networks, which:
- Mimic real brain neurons firing only when needed (sparse computation)
- Encode time-dependent signals efficiently
- Are ideal for event-driven, real-time factory environments
This makes NeuroMold not just another AI controller — it’s neuromorphic intelligence at the edge.
- 🧠 SNN-based controller using LIF neurons for adaptive control
- ⚡ Energy-efficient inference measured via spike counts
- 🧪 Simulated real-world testbed for evaluating control strategies
- 📊 Dashboard visualization of metrics like spike rate, energy vs accuracy
- 🔄 Modular architecture for future hardware integration (e.g. Intel Loihi)
- Python 3.9+
- PyTorch
- Norse
- NumPy, Matplotlib
cd Simulations
python run_pipeline.py.npyarrays with SNN/PID/ANN controller outputs- Charts in
visualizations/output_charts/
cd Dashboard
npm install
npm run devThis starts the dashboard at http://localhost:5173/ with live graphs for spike activity, controller comparison, and more.
@misc{achari2025neuromold,
author = {Vibusha S Achari},
title = {NeuroMold: Neuromorphic Adaptive Control for Injection Molding},
year = {2025},
howpublished = {\url{https://github.com/VibuAchari/NeuroMold}},
}- Integrating real-time sensor streaming via MQTT
- Optimizing spike encoding through self-adaptive schemes
- Porting to neuromorphic hardware (Intel Loihi, SpiNNaker)
- Filing a patent and industrial pilot deployment
NeuroMold aims to push the frontier of bio-inspired intelligent manufacturing — bringing brains to the factory floor, one spike at a time. ⚡🧠🏭